Scientific registration n° : 2042 Symposium n° : 20 Presentation : poster
Spatial and temporal variability of factors controlling the susceptibility to erosion on a sugar beet field plot Variabilité spatiale et temporelle des facteurs contrôlant le risque d'érosion dans une placette expérimentale de betteraves sucrières FOHRER Nicola1, MÜHLHOFF Bert1, FREDE Hans-Georg 1 1
Department of Agricultural Ecology and Natural Resources Management. Division of Soil and Water Protection, Giessen University, Senckenbergstr. 3, D-35390 Giessen. Email:
[email protected]
1. Introduction Extended research has been perfomed on interrill erosion processes and the factors controlling them, but the results are mostly based on point measurements in the field or on laboratory studies using a rainfall simulator (Sumner and Steward, 1992; Roth et al., 1995; Fohrer et al., 1998). Few attempts have been made to determine spatial patterns of the involved processes on a field scale. For basic soil properties like clay content, organic carbon or pH value, existing studies differ widely in the observed scale (Beckett and Webster, 1971). Studies concerning the spatial variability of interrill erosion are mostly carried out on very small areas. Farres and Muchena (1996) investigated spatial crusting patterns under developing plant cover using photogrametry and a GIS- System for the data analysis. The experiments were conducted on small laboratory plots (75 cm * 45 cm) applying simulated rainfall. Bilders et al. (1996) focused on the spatial patterns of surface crusts on 1 m² field plots under natural rainfall in Africa. They combined micomorphological crust studies with observations of the surface microrelief. The surface colour of the plots was mapped at the time of crust sampling. Soil texture and colour were consistent with the crust type found in each mapping unit. The objective of this study was the investigation of spatial and temporal variability of basic soil properties in combination with parameters indicating the susceptibility to interrill erosion on a field scale. 2. Materials and Methods Sampling site The sampling site is situated at the experimental field station Rauischholzhausen, which belongs to Giessen University, Germany. The mean annual precipitation varies between 1
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Figure 1: Precipitation and cumulated rainfall energy
650 and 700 mm and the annual mean temperature is 8.8 - 9 °C. The soil studied is a silty Haplic Luvisol. Soil samples were taken only from the Ap- horizon, which is a silty clay loam and therefore expected to be susceptible to surface sealing. The field was tilled in early November 1996 and leveled with a field cultivator. Sugar beet was sown in early April 1997 using a sower and a spike harrow in combination. Thus the resulting microrelief of the seedbed was rough. Rainfall was recorded automatically at 5 min. intervals with a rainfall gauge at the experimental station close by (Fig. 1), so that the kinetic rainfall energy could be calculated using a formula derived from Wischmeyer and Smith, 1958. Sampling design and measurement methods An unbalanced hierarchical sampling design (Webster and Olivier, 1990) with 36 sampling points (Fig. 2) was used in a sugar North South beet field over an area of 8100 m² to investigate the spatial variability of aggregate stability, C/N- ratio, soil moisture, bulk East density, grain size Figure 2: Sampling pattern distribution, pH-value and saturated hydraulic conductivity of the unsealed soil. Three to four replications West
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were performed for each sampling point and parameter. Soil moisture and bulk density were also measured in a weekly time step. The first samples were taken immediately after sowing of the sugar beet in early April 1997, the last samples were taken shortly before total soil coverage by sugar beet leaves at the beginning of July. At 9 of the 36 sampling points a roughness index and surface sealing index were determined in a 6 weekly time step. For the characterization of the surface roughness a simple field index developed by Rudolph (1997) was determined. Mapping areas of 42.0 * 59.4 cm each were marked on the field and the outlines of all aggregates larger than 1 cm in diameter were drawn on transparencies. These were photocopied, scanned, aggregate areas blackened and then the specific surface area (SSA) index for the aggregates covering the surface was calculated. The hydraulic conductivity of the sealed soil core samples was measured as proposed by Bohl and Roth (1993). Twelve repetitions were performed for each sampling point and term. The ratio of the hydraulic conductivity of unsealed vs. sealed sample was calculated to describe the degree of surface sealing at each time step. The thickness of the seal was measured so that the hydraulic conductivity of the sealed layer could be calculated, using the approach of Darcy for a two layer system, as proposed by Roth (1992). Aggregate stability was measured with a modified percolation method (Sekera and Brunner, 1943). 3. Results and Discussion Basic soil properties (36 sampling points) Table 1 shows descriptive statistics of the soil properties investigated. In this case the coefficient of variance for the 36 sampling points is used as a measure for spatial heterogeneity and not as a measure for the accuracy of the measurement. The highest variation was found for aggregate stability and saturated hydraulic conductivity. The hydraulic conductivity of the top horizon was very high due to by seedbed preparation shortly before sampling. This corresponds also with the low initial bulk density. Due to the application of fertilizer and lime, pH and total nitrogen content were relatively homogeneously distributed. Clay and organic matter content showed a spatial pattern connected to transport processes. Enrichment and impoverishment areas were closely correlated to the elevation.
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Table 1: Descriptive statistics of basic soil properties Parameter Mean
Clay (%) Silt (%) Sand (%) Organic matter (%) Nt (%) Saturated hydr. conductivity, 3.4.1997 (cm/d) pH Aggregate stability Bulk density, 3.4.1997 Bulk density, 26.6.1997
19.07 73.18 7.75 1.60 0.18 2057 6.13 55.64 1.07 1.11
Coefficient of variance (%) 9 2 7 10 8 13 3 36 7 4
Min
Max
16.20 69.42 6.34 1.25 0.14 1298 5.73 24.90 0.97 1.02
22.88 76.02 8.81 1.92 0.21 2538 6.46 97.80 1.25 1.19
For all properties investigated a multiple regression analysis was conducted. The correlations between basic soil properties proved to be weak due to their small range. For regionalization of all measured basic soil properties, variogram analysis (Matheron, 1963) was performed. Aggregate stability had to be first ln-transformed to achieve a normal distribution. All other parameters were normally distributed. The results of the variogram analysis are shown in Table 2. The variograms were fitted with a spherical model, apart from the variogram of aggregate stability, which was fitted with a Gaussian model. The ratio of nugget to total variance, expressed as a percentage according to Cambardella et al. (1994), was calculated to characterize the degree of spatial dependency. Values smaller than 25% indicate a strong spatial dependence, whereas values between 25 and 75% are moderate in their spatial relationship. Table 2: Variograms of basic soil properties Parameter Direction of Sill Range Nugget Nugget/Sill variogram m (%) east/west 4.21 30.14 0.26 6 Clay north/south 3.49 47.54 1.5 43 none 2.96 31.64 0.56 19 Silt none 0.22 47.28 0.031 14 Sand none 0.0257 30.79 0.00256 10 Organic matter none 0.00021 25.66 0.000077 37 Nt 31521 18.37 13806 44 Saturated hydr. none conductivity, 3.4.1997 none 0.026 30.71 0.006 23 pH none 0.15 18.36 0.02 13 Aggregate stability none 0.0052 16.17 0.0025 47 Bulk density, 3.4.1997 0.0022 26.41 0.0017 76 Bulk density, 26.6.1997 none Soil texture, organic matter, pH and aggregate stability proved to be highly spatially dependent. For total nitrogen content and initial saturated hydraulic conductivity, the spatial dependence was moderate. The observed spatial relationships are strongly influenced by the scale of investigation (Cambardella et al., 1994). For spatial 4
interpolation ordinary Kriging was carried out. Contour maps were used to display the spatial patterns. Temporal variability bulk density and water content (36 sampling points) In Figures 3 and 4 the temporal variations of water content and bulk density are shown as a mean of 36 sampling points. The standard deviation is in this case the result of spatial variability. The 30 surface moisture curve clearly reflects the rainfall pattern (Fig.1). The 20 standard deviation is low for samples taken shortly after rainfall. The heterogeneity increases 10 with the number of dry days before sampling. In dry periods the spatial pattern of water content 0 0 20 40 60 80 was very similar to the spatial distribution of clay days after sowing [d] and organic matter. After Figure 3: Temporal variation of water content (0-5 cm) sowing, the bulk density decreases due to the effects 1,15 of freezing and thawing. After this period the bulk density increases steadily due to natural leveling and 1,10 the effect of raindrop impact inducing crust formation processes. The 1,05 total increase in bulk density is relatively low, although it should be remembered that the bulk 1,00 density value (0-5cm) 0 20 40 60 80 represents a mean of sealed days after sowing [d] and unsealed layer and the Figure 4: Temporal variation of bulk density (0-5 cm) bulk density of the seal is expected to be much higher (Fohrer et al., 1998). Indices of interrill erosion (9 sampling points) A decrease in saturated hydraulic conductivity (Table 3) with time due to surface sealing can be observed, alhough the absolute values are still high. The variation due to spatial variability is higher than the variation within the repetitions (tested with analysis of variance). The surface roughness, characterized with the SSA-Index, decreases as a 5
result of the leveling effect of raindrop impact. The ratio of final and initial SSA is a measure for the degree of leveling. Table 3: Descriptive statistics of interrill erosion indices Index Mean 2151 1199 1153 398 271 18.20 14.59 14.06
Kf1, 3.4. 1997, (cm/d) Kf2, 12.5. 1997, (cm/d) Kf3, 7.7. 1997, (cm/d) Kseal1, 12.5. 1997, (cm/d) Kseal2, 7.7. 1997, (cm/d) SSA1, 25.4.1997, (%) SSA2, 12.5.1997, (%) SSA3, 7.7.1997, (%)
Coefficient Min of variance 0.17 1298 0.12 919 0.23 797 0.35 214 0.50 144 0.19 12.99 0.31 9.03 0.35 7.63
Max 2538 1400 1450 676 487 23.26 23.86 20.95
Kf = saturated hydraulic conductivity of (sealed) soil samples, Kseal = saturated hydraulic conductivity of sealed layer, SSA = specific surface area, index number = sampling term
The decrease of surface roughness (SSA3 / SSA1) is R² correlated to aggregate sta0.80 bility and pH value (Table 0.57 4). The initial saturated 0.71 hydraulic conductivity (Kf1) 0.80 is closely connected to the initial surface roughness (SSA1) and aggregate stability. The saturated hydraulic conductivity (kf3) of the sealed samples is a function of the decreasing surface roughness and aggregate stability. Table 4: Regression analysis for erosion indices Regression (SSA3 / SSA1) = f(pH, ln aggregate stability) Kf1 = f(SSA1; ln aggregate stability) Kf3 = f(SSA3 / SSA1) Kf3 = f(SSA3 / SSA1; ln aggregate stability)
4. Conclusions The spatial and temporal variability of interrill erosion indices was quantified on a 8100m² sugar beet plot. The spatial distribution of the indices was connected to the spatial patterns of basic soil properties, such as pH and aggregate stability. The decrease of saturated hydraulic conductivity is accompanied in space and time by decreasing surface roughness. In further studies, a larger number of sampling points should be contemplated, to allow variogram analysis also for the chosen interrill erosion indices. Additionally, a quicker and more accurate method for determination of Kseal would be desirable. References Beckett, P.H.T, and R. Webster, 1971: Soil variability: A review. Soils Fertilizers, 34(1): 1-15. Bilders, C.L., P. Baveye, L.L. Wilding, L.R. Drees and C. Valentin, 1996: Tillageinduced spatial distribution of surface crusts on a sandy Paleustult from Togo. Soil Sci. Soc. Am. J., 60: 843-855. Bohl, H. and C.H. Roth, 1993: A simple method to assess the susceptibility of soils from surface seals under field conditions. Catena, 20: 247-256. 6
Cambardella, C.A., T.B. Moormann, J.M. Novak, T.B. Parkin, and D.L. Kanopka, 1994: Field-scale variability of soil properties in central Iowa soils. Soil Sci. Soc. Am. J., 58: 1501-1511. Farres, P.J., and J. Muchena, 1996: Spatial patterns of soil crusting and their relationship to cover crop. Catena, 26: 247-260. Fohrer, N., J. Berkenhagen, J.-M. Hecker and A. Rudolph, 1998: Changing Soil and Surface Conditions During Rainfall -Single Rainstorm /Subsequent Rainstorms. Accepted by Catena. Matheron, G., 1963: Principles of geostatistics. Economic Geology, 58: 1246-1266. Roth, C.H., 1992: Die Bedeutung der Oberflächenverschlämmung für die Auslösung von Abfluß und Abtrag. Habilitation. Technical University Berlin, Bodenökologie und Bodengenese, No. 6, 179 p. Roth, C.H., K. Helming, and N. Fohrer, 1995: Surface sealing and runoff generation on soils derived from loess and Pleistocene deposits. Z. Pflanzenernähr. Bodenk., 158: 43-53. Rudolph, A., 1997: Das Mikrorelief als Initialprozeß der OberflächenabflußbildungAbleitung eines Kennwertes zu seiner Charakterisierung. Ph.D. Thesis, Technical University Berlin, Bodenökologie und Bodengenese, No. 24, 177 p. Sekera, F., and A. Brunner, 1943: Beiträge zur Methodik der Gareforschung. Z. Pflanzenernähr. Bodenk., 29: 181-212. Sumner, M.E., and B.A. Stewart, (eds.), 1992: Soil crusting: Chemical and physical processes. Adv. Soil Sci., 365 p. Webster, R. and M.A. Olivier, 1990: Statistical methods in soil and land resource survey. Oxford University Press, 316 p. Wischmeyer, W.H., and D.D. Smith, 1958: Rainfall energy and its relationship to soil loss. Trans. Amer. Geophys. Union, 39(2): 285-291. Keywords : interrill erosion, spatial variability, surface sealing and roughness, geostatistics, bulk density Mots clés : érosion en inter-rigole, variabilité spatiale, croûtes de battance, rugosité de surface, géostatistique, densité apparente
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